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Mota Carvalho TF, Santos VLA, Silva JCF, Figueredo LJDA, de Miranda SS, Duarte RDO, Guimarães FG. A systematic review and repeatability study on the use of deep learning for classifying and detecting tuberculosis bacilli in microscopic images. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 180-181:1-18. [PMID: 37023799 DOI: 10.1016/j.pbiomolbio.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/11/2023] [Accepted: 03/21/2023] [Indexed: 04/08/2023]
Abstract
Tuberculosis (TB) is among the leading causes of death worldwide from a single infectious agent. This disease usually affects the lungs (pulmonary TB) and can be cured in most cases with a quick diagnosis and proper treatment. Microscopic sputum smear is widely used to diagnose and manage pulmonary TB. Despite being relatively fast and low cost, it can be exhausting because it depends on manually counting TB bacilli (Mycobacterium tuberculosis) in microscope images. In this context, different Deep Learning (DL) techniques are proposed in the literature to assist in performing smear microscopy. This article presents a systematic review based on the PRISMA procedure, which investigates which DL techniques can contribute to classifying TB bacilli in microscopic images of sputum smears using the Ziehl-Nielsen method. After an extensive search and a careful inclusion/exclusion procedure, 28 papers were selected from a total of 400 papers retrieved from nine databases. Based on these articles, the DL techniques are presented as possible solutions to improve smear microscopy. The main concepts necessary to understand how such techniques are proposed and used are also presented. In addition, replication work is also carried out, verifying reproducibility and comparing different works in the literature. In this review, we look at how DL techniques can be a partner to make sputum smear microscopy faster and more efficient. We also identify some gaps in the literature that can guide which issues can be addressed in other works to contribute to the practical use of these methods in laboratories.
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Affiliation(s)
- Thales Francisco Mota Carvalho
- Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil; Institute of Engineering, Science and Technology, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Av. Um, 4.050, Janaúba, 39447-814, MG, Brazil
| | - Vívian Ludimila Aguiar Santos
- Institute of Engineering, Science and Technology, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Av. Um, 4.050, Janaúba, 39447-814, MG, Brazil; Instituto Federal do Norte Minas Gerais, Rua Humberto Mallard 1355, Pirapora, 39274-140, MG, Brazil
| | | | - Lida Jouca de Assis Figueredo
- Faculdade de Medicina, Laboratório de pesquisa em micobactérias, Universidade Federal de Minas Gerais, Av. Alfredo Balena, 190, Belo Horizonte, 30130-100, MG, Brazil
| | - Silvana Spíndola de Miranda
- Faculdade de Medicina, Laboratório de pesquisa em micobactérias, Universidade Federal de Minas Gerais, Av. Alfredo Balena, 190, Belo Horizonte, 30130-100, MG, Brazil
| | - Ricardo de Oliveira Duarte
- Department of Electronics, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte, MG, Brazil
| | - Frederico Gadelha Guimarães
- Machine Intelligence and Data Science (MINDS) Laboratory, Department of Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil.
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Euliano EM, Sklavounos AA, Wheeler AR, McHugh KJ. Translating diagnostics and drug delivery technologies to low-resource settings. Sci Transl Med 2022; 14:eabm1732. [PMID: 36223447 PMCID: PMC9716722 DOI: 10.1126/scitranslmed.abm1732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Diagnostics and drug delivery technologies engineered for low-resource settings aim to meet their technical design specifications using strategies that are compatible with limited equipment, infrastructure, and operator training. Despite many preclinical successes, very few of these devices have been translated to the clinic. Here, we identify factors that contribute to the clinical success of diagnostics and drug delivery systems for low-resource settings, including the need to engage key stakeholders at an early stage, and provide recommendations for the clinical translation of future medical technologies.
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Affiliation(s)
- Erin M. Euliano
- Department of Bioengineering, Rice University; Houston, Texas 77005, USA
| | - Alexandros A. Sklavounos
- Department of Chemistry, University of Toronto; Toronto, Ontario M5S 3H6, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto; Toronto, Ontario M5S 3E1, Canada
| | - Aaron R. Wheeler
- Department of Chemistry, University of Toronto; Toronto, Ontario M5S 3H6, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto; Toronto, Ontario M5S 3E1, Canada
- Institute of Biomedical Engineering, University of Toronto; Toronto, Ontario M5S 3G9, Canada
| | - Kevin J. McHugh
- Department of Bioengineering, Rice University; Houston, Texas 77005, USA
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